• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.127-127
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• 2019

농어촌 공사 주관 3,000여개가 넘는 농업 저수지가 있지만, 대부분의 저수지 공급량은 수문관리원에 의해 관행적 방법에 의해 관리되고 있다. 수문상황은 악화되고, 농업용수의 관개목적 외에 다목적용수로 고려됨에 따라 효율적인 농업용수의 사용은 필수적이게 되었다. 효율적인 농업용수 관리를 위해서는 정량적 자료가 필수적이나 현재로서는 관행적 방법으로 관리가 되기에 공급량 자료를 갖고 있는 저수지는 극히 드문 실정이다. 따라서 일반적으로 농업용수의 공급량은 필요수량에 근거하여 추정하는 방법이 사용되고 있다. 그러나 이러한 방법으로 추정된 공급량과 실제 공급량에서는 많은 차이가 있어 농업용수의 정략적인 관리에 어려움이 되고 있다. 본 연구에서는 이러한 문제를 해결하고자 데이터에 기반하여 관행을 반영한 공급량을 추정하고자 하였다. 농업용수 공급량은 물수지식에 기반한 순별 강수량-공급량 회귀식을 구축하여 추정하였다. 선형계수를 도입하여 유입량을 불확실성을 고려하였으며, 가뭄년도의 평년관행과는 다른 가뭄년도의 물공급 관행을 반영할 수 있도록 평년 공급량에 공급량 보정계수를 적용하는 방법을 제시하였다. 모형의 검증을 위해 충청남도 홍성군에 위치한 대사, 공리 저수지와 경기도 안성시에 위치한 만수 저수지를 선정하였으며, 대사, 공리저수지의 인근 기상청인 서산과 만수저수지의 인근 기상청인 이천 기상청에서 기상관측 자료를 수집하였다. 과거 15년간(2002년~2016년)의 관측저수율과 공급량모의를 활용한 모의 저수율 추적을 통해 비교해보았다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.39-39
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• 2018

우리나라는 전체 국토의 약 70%가 산악지형으로 이루어져 있고 연중 강우가 6월에서 9월에 집중되는 기후적 특성을 가지고 있다. 최근 기후변화의 영향까지 더해지면서 시간당 300mm 이상의 집중호우를 보이는 이상강우가 빈번하게 발생하고 있다. 대부분의 도시지역은 하천을 중심으로 발달되어 있어 인구 및 사회기반시설의 집약정도가 매우 높고 하천변 저지대 지역에 주거 및 상업시설이 밀집되어 있다. 기후적 지역적 특성으로 인한 홍수피해를 미연에 방지하고 피해를 최소화하기 위하여 치수 중심의 수자원 관리를 위해 노력하고 있다. 하지만 우리나라의 하천관리는 시기별 하천 수량의 급격한 변동으로 어려움을 겪고 있다. 이러한 어려움을 극복하고 효율적인 수자원 관리 및 홍수피해 저감을 위해 수계를 중심으로 20개의 다목적댐을 건설하여 운영 관리 중에 있다. 특히, 홍수기 시 댐 운영은 예상 강우에 따라 적절한 예비방류와 강우 시 효율적인 댐 운영계획이 필수적이다. 본 연구에서는 강우가 집중되는 홍수기 댐 운영 시에 예상 강우량에 따라 댐 유역 내 유량 증가에 기여하는 정도를 예측할 수 있는 유출율 예측 회귀식을 개발하였다. 유출율은 강우와 유출량의 비로 지역특성, 강우특성, 관개여부, 선행강우량, 강우이동 방향 등 다양한 요인에 의해 복잡한 메케니즘을 갖는다. 단순히 예상되는 총강우량에 따른 유출율 만으로 상호관계를 정의하기가 쉽지 않기 때문에 한국수자원공사에서 개발한 댐군 홍수조절 연계운영시스템(COSFIM)인 수문학적 연계운영모형을 활용하였다. 최근 10년간 홍수기에 발생한 강우사상별 시간단위의 수문자료(총강우량, 기저유량, 유출율, 무강우일수, 강우지속시간 등) 분석을 실시하였다. COSFIM 모형을 통한 결과를 토대로 고려항목 간 교차검증을 통해 사분위수범위의 이상치 경계를 설정하고 상관분석 결과에 따라 0.5 이상의 상관성이 높은 항목을 활용하여 예측 강우량에 따른 유출율 예측 회귀식을 도출하였다. 본 연구에서 개발한 예측 강우에 따른 유출율 예측 산정식은 댐 유역에 예상되는 강우량에 대하여 하천의 유량 증가 예측 정도를 정량적으로 제시할 수 있으며, 실제 홍수기 댐 운영 시 예상 강우량에 따라 신속하고 적절한 수문 방류 계획 수립에서 용이하게 활용할 수 있을 것으로 기대한다.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.85-96
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• 2021

Irrigation return flow is defined as the excess of irrigation water that is not evapotranspirated by direct surface drainage, and which returns to an aquifer. It is important to quantitatively estimate the irrigation return flow of the water cycle in an agricultural watershed. However, the previous studies on irrigation return flow rates are limitations in quantifying the return flow rate by region. Therefore, simulating irrigation return flow by accounting for various water loss rates derived from agricultural practices is necessary while the hydrologic and hydraulic modeling of cultivated canal-irrigated watersheds. In this study, the irrigation return flow rate of agricultural water, especially for the entire agricultural watershed, was estimated using the SWMM (Storm Water Management Model) module from 2010 to 2019 for the Madun reservoir located in Anseong, Gyeonggi-do. The results of SWMM simulation and water balance analysis estimated irrigation return flow rate. The estimated average annual irrigation return flow ratio during the period from 2010 to 2019 was approximately 55.3% of the annual irrigation amounts of which 35.9% was rapid return flow and 19.4% was delayed return flow. Based on these results, the hydrologic and hydraulic modeling approach can provide a valuable approach for estimating the irrigation return flow under different hydrological and water management conditions.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.673-683
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• 2016

An Idea to estimate flexible criteria for river water use permits was proposed that takes the spatio-temporal flow variation along the river into account, which was applied to the Keumho River, one of the tributary of the Nakdong River in Korea. This idea implies the temporal division of four periods with different criteria, combining flood/non-flood seasons and irrigation/non-irrigation periods, while a single one has been applied throughout the year in the current practice. Through flow regime analysis of daily natural flow simulations at Dongchon and Seongseo, the control points of the study area, Q355 and 1Q10 for non-flood and non-irrigation period, Q275 for non-flood and irrigation period, Q185 for flood and irrigation period were suggested respectively. So, those values that subtract instream flow were determined as the flexible criteria in each season. From the comparison of current practice and the proposed method, it was estimated that $10.6\;million\;m^3/year$ is available for more water use permits without additional development of water storage. Therefore, it is conceived that flexible criteria for river water use permission suggested in this study can contribute to improve the national policies for more efficient water resources management in the future.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.4
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• pp.386-408
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• 1988

A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.4
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• pp.452-457
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• 2009

The objective of this study was to provide fundamental data on breeding cultivar and cultural technique to identify quality characterization according to damage degrees in rice when are damaged at low temperature. For induction of cold damage, we treated the irrigation water at $17^{\circ}C$ from the panicle formation stage to the heading date. The rice products were harvested by grades according to the sterility ratio and investigated 5 items of quality analysis including ripened grain ratio, brown/rough rice ratio, 1000 grain weight of brown rice, protein content, and amylose content. The quality analysis were characterized by each items according to the sterility ratio. As a result, the ripened grain ratio was y=1.0444x-7.6597($R^2=0.9874^{**}$), protein content was y=-0.046x+10.875 ($R^2=0.6973^*$), and head rice ratio was y=-0.2306x+104.32 ($R^2=0.634^*$), but the amylose content, brown/rough rice ratio and the milled/brown rice ratio were not significant. The rice plants, which injured by the low temperature, had bad influence in the yield and quality. Consequently, the breeding of rice cultivar and development of cultural technique are required to improve its cold tolerance.